Statistics and Probability Commons^™

Traditional Vs Machine Learning Approaches: A Comparison Of Time Series Modeling Methods, Miguel E. Bonilla Jr., Jason Mcdonald, Tamas Toth, Bivin Sadler

SMU Data Science Review

In recent years, various new Machine Learning and Deep Learning algorithms have been introduced, claiming to offer better performance than traditional statistical approaches when forecasting time series. Studies seeking evidence to support the usage of ML/DL over statistical approaches have been limited to comparing the forecasting performance of univariate, linear time series data. This research compares the performance of traditional statistical-based and ML/DL methods for forecasting multivariate and nonlinear time series.

A Hybrid Ensemble Of Learning Models, Bivin Sadler, Dhruba Dey, Duy Nguyen, Tavin Weeda

SMU Data Science Review

Statistical models in time series forecasting have long been challenged to be superseded by the advent of deep learning models. This research proposes a new hybrid ensemble of forecasting models that combines the strengths of several strong candidates from these two model types. The proposed ensemble aims to improve the accuracy of forecasts and reduce computational complexity by leveraging the strengths of each candidate model.

Indirect Aggression And Victimization: Investigating Instrument Psychometrics, Gender Differences, And Its Relationship To Social Information Processing, Taylor Steeves

Electronic Theses and Dissertations

The study of indirect bullying behaviors, relational aggression and social aggression, has been of theoretical importance and interest to researchers and psychologists within the last few decades. In this investigation, using a convenience sample of 451 late adolescents attending a private university in the mid-Atlantic U.S., I examined the factor structure of two measures of indirect bullying, the Young Adult Social Behavior Scale – Victim (YASB-V) and the Young Adult Social Behavior Scale – Perpetrator (YASB-P). Using confirmatory factor analysis (CFA), I found that the YASB-V comprised a four-factor model, differing from the model that had been identified in the …

The "Benfordness" Of Bach Music, Chadrack Bantange, Darby Burgett, Luke Haws, Sybil Prince Nelson

Journal of Humanistic Mathematics

In this paper we analyze the distribution of musical note frequencies in Hertz to see whether they follow the logarithmic Benford distribution. Our results show that the music of Johann Sebastian Bach and Johann Christian Bach is Benford distributed while the computer-generated music is not. We also find that computer-generated music is statistically less Benford distributed than human- composed music.

Math And Democracy, Kimberly A. Roth, Erika L. Ward

Journal of Humanistic Mathematics

Math and Democracy is a math class containing topics such as voting theory, weighted voting, apportionment, and gerrymandering. It was first designed by Erika Ward for math master’s students, mostly educators, but then adapted separately by both Erika Ward and Kim Roth for a general audience of undergraduates. The course contains materials that can be explored in mathematics classes from those for non-majors through graduate students. As such, it serves students from all majors and allows for discussion of fairness, racial justice, and politics while exploring mathematics that non-major students might not otherwise encounter. This article serves as a guide …

The Importance Of Contrast Sensitivity, Color Vision, And Electrophysiological Testing In Clinical And Occupational Settings, Frances Silva

Theses & Dissertations

Visual acuity (VA) is universally accepted as the gold standard metric for ocular vision and function. Contrast sensitivity (CS), color vision, and electrophysiological testing for clinical and occupational settings are warranted despite being deemed ancillary and minimally utilized by clinicians. These assessments provide essential information to subjectively and objectively quantify and obtain optimal functional vision. They are useful for baseline data and monitoring hereditary and progressive ocular conditions and cognitive function. The studies in this dissertation highlight the value of contrast sensitivity, color vision, and cone specific electrophysiological testing, as well as the novel metrics obtained with potential practical clinical …

Causal Inference Methods For Estimation Of Survival And General Health Status Measures Of Alzheimer’S Disease Patients, Ehsan Yaghmaei

Computational and Data Sciences (PhD) Dissertations

Identifying optimal treatment options with respect to survival of Alzheimer's disease patients is crucially important and previously uninvestigated research question. Our objective was to estimate the causal effects of the most prevalent classes of Alzheimer’s disease drugs, Donepezil and Memantine, and their combined use on Survival and General Health Status Measures of Alzheimer's disease patients for the first five years after initial diagnosis. We carried out a thorough causal inference study using doubly robust estimators, nonparametric bootstrap confidence intervals, Bonferroni corrections for multiple comparisons and analyzing one of the largest high-quality medical databases containing millions of de-identified electronic health records …

Stochastic Processes And Multi-Resolution Analysis: A Trigonometric Moment Problem Approach And An Analysis Of The Expenditure Trends For Diabetic Patients, Isaac Nwi-Mozu

Computational and Data Sciences (PhD) Dissertations

This dissertation is divided into two distinct parts. The main theme of the first part is to study stochastic processes (and related signal processing questions) using tools in wavelet analysis, functional analysis (we use in particular the trigonometric moment problem), the theory of realization of rational functions, and reproducing kernel Hilbert spaces. A novel form of multiresolution analysis is formulated in the discrete case that is used to study some stochastic processes. Using the trigonometric moment problem, we associate with a vector-valued wide-sense stationary process a multiresolution of a new kind. The notion of realization of rational functions was used …

Modeling Biphasic, Non-Sigmoidal Dose-Response Relationships: Comparison Of Brain- Cousens And Cedergreen Models For A Biochemical Dataset, Venkat D. Abbaraju, Tamaraty L. Robinson, Brian P. Weiser

Rowan-Virtua School of Osteopathic Medicine Departmental Research

Biphasic, non-sigmoidal dose-response relationships are frequently observed in biochemistry and pharmacology, but they are not always analyzed with appropriate statistical methods. Here, we examine curve fitting methods for “hormetic” dose-response relationships where low and high doses of an effector produce opposite responses. We provide the full dataset used for modeling, and we provide the code for analyzing the dataset in SAS using two established mathematical models of hormesis, the Brain-Cousens model and the Cedergreen model. We show how to obtain and interpret curve parameters such as the ED50 that arise from modeling, and we discuss how curve parameters might change …

Statistical Inference On Lung Cancer Screening Using The National Lung Screening Trial Data., Farhin Rahman

Electronic Theses and Dissertations

This dissertation consists of three research projects on cancer screening probability modeling. In these projects, the three key modeling parameters (sensitivity, sojourn time, transition density) for cancer screening were estimated, along with the long-term outcomes (including overdiagnosis as one outcome), the optimal screening time/age, the lead time distribution, and the probability of overdiagnosis at the future screening time were simulated to provide a statistical perspective on the effectiveness of cancer screening programs. In the first part of this dissertation, a statistical inference was conducted for male and female smokers using the National Lung Screening Trial (NLST) chest X-ray data. A …

A Comparative Study Of Techniques For Non-Monotonic Dependence With Emphasis On Sensitivity To Sample Size, Noise Level And Computational Attributes, Fariha Tasnim

Graduate Theses and Dissertations

Evaluating association between variables is often of interest by many researchers. To serve this purpose, different association measures have been developed. However, type of relation between variables affects the degree of relationship. Hence, detection of the rela- tionship between variables is germane to measuring the correlation coefficient. With that mindset, here we explored six non-monotonic measure of association techniques and com- pared them with three classical approaches. Due to inconsistency in definition and range of different techniques, it is not feasible to compare the correlation estimates as their nature of variability differ. Therefore, we used permutation test based on Monte …

Exploring Experimental Design And Multivariate Analysis Techniques For Evaluating Community Structure Of Bacteria In Microbiome Data, Kelsey Karnik

Department of Statistics: Dissertations, Theses, and Student Research

The gut microbiome plays a crucial role in human health, and by working collaboratively with microbiologists, we aim to further our understanding of the human gut and its impact on human health. Promoting a diverse microbiome is emphasized throughout microbiology literature, and involving a statistician in designing experiments to relate gut bacteria and some measured health outcome is crucial for ensuring valid and accurate results. By adopting new experimental design and analysis methods, researchers can begin to gain a deeper understanding of how the genetics of our food affect the composition of taxa within the gut microbiome. This dissertation is …

A Framework For Statistical Modeling Of Wind Speed And Wind Direction, Eva Murphy

All Dissertations

Atmospheric near surface wind speed and wind direction play an important role in many applications, ranging from air quality modeling, building design, wind turbine placement to climate change research. It is therefore crucial to accurately estimate the joint probability distribution of wind speed and direction. This dissertation aims to provide a modeling framework for studying the variation of wind speed and wind direction. To this end, three projects are conducted to address some of the key issues for modeling wind vectors.\\

First, a conditional decomposition approach is developed to model the joint distribution of wind speed and direction. Specifically, the …

Cannabidiol Tweet Miner: A Framework For Identifying Misinformation In Cbd Tweets., Jason Turner

Electronic Theses and Dissertations

As regulations surrounding cannabis continue to develop, the demand for cannabis-based products is on the rise. Despite not producing the psychoactive effects commonly associated with THC, products containing cannabidiol (CBD) have gained immense popularity in recent years as a potential treatment option for a range of conditions, particularly those associated with pain or sleep disorders. However, due to current federal policies, these products have yet to undergo comprehensive safety and efficacy testing. Fortunately, utilizing advanced natural language processing (NLP) techniques, data harvested from social networks have been employed to investigate various social trends within healthcare, such as disease tracking and …

Copula Based Models For Bivariate Zero-Inflated Count Time Series Data, Dimuthu Fernando

Mathematics & Statistics Theses & Dissertations

Count time series data have multiple applications. The applications can be found in areas of finance, climate, public health and crime data analyses. In most scenarios, time is an important part of the data. Time series counts then come as multivariate vectors that exhibit not only serial dependence within each time series but also with cross-correlation among the series. When considering these observed counts, and when a value, say zero, occurs more often than usual, analysis presents crucial challenges. There is presence of zeroinflation in the data. The literature on bivariate or multivariate count time series, as well as zero-inflated …

Weighted Mean Difference Statistics For Paired Data In The Presence Of Missing Values, Yuntong Li, Brent J. Shelton, William St Clair, Heidi L. Weiss, John L. Villano, Arnold Stromberg, Chi Wang, Li Chen

Markey Cancer Center Faculty Publications

Missing data is a common issue in many biomedical studies. Under a paired design, some subjects may have missing values in either one or both of the conditions due to loss of follow-up, insufficient biological samples, etc. Such partially paired data complicate statistical comparison of the distribution of the variable of interest between the two conditions. In this article, we propose a general class of test statistics based on the difference in weighted sample means without imposing any distributional or model assumption. An optimal weight is derived from this class of tests. Simulation studies show that our proposed test with …

Statistical Modeling Approaches For The Inference Of Cancer Mechanisms, Licai Huang

Dissertations and Theses (Open Access)

The aim of this study was to explore the potential of integrating multi-platform genomic datasets to improve our understanding of the biological mechanisms behind cancer. By merging clinical outcomes with the data obtained from multi-platform genomic studies, we can gain insight into the biological mechanisms behind a patient’s response to treatment. Additionally, the evaluation of the correlations between genetic variations and gene expression provides a better understanding of the functional significance of these variations. Such knowledge has the potential to revolutionize cancer diagnosis and treatment. This thesis describes methods developed to address two related aims. Aim 1: We have developed …

Statistical Approaches To Estimate Bidirectional And Time-Varying Causal Effects Using Mendelian Randomization, Jinhao Zou

Dissertations and Theses (Open Access)

Mendelian Randomization (MR) is an epidemiological framework using genetic variants as instrumental variables (IVs) to examine the causal effect of an exposure on an outcome. It is widely used to detect causal factors of diseases and provide insight into the biological pathway of diseases. Current methods under the MR framework are built to estimate the unidirectional causal effects of exposures on outcomes and neglect the potential bidirectional causal effects. However, a bidirectional causal effect creates a feedback loop that biases the casual inference in MR studies. Furthermore, current MR methods estimate the causal effect as a single value using cross-sectional …

Single-Index Multinomial Model For Analyzing Crime Data, Kwabena Gyamfi Duodu

Open Access Theses & Dissertations

We develop a flexible single-index multinomial model for analyzing crime data. In additionto the number of crimes reported, the data also includes covariates such as location, time of day, weather, and other demographic factors. We provide an estimation algorithm and develop R code for the single-index multinomial model. Using simulations, we evaluate the performance of the proposed estimation algorithm. When applied to crime data, the single-index multinomial model provides important insights into crime trends and risk variables, assisting in the development of tailored crime prevention programs. Policymakers and law enforcement organizations can use the model's projections to more efficiently allocate …

Comparative Study Of Supervised Classification Techniques With A Modified Knn Algorithm, Noah Owusu

Open Access Theses & Dissertations

The goal of classification is to develop a model that can be used to accurately assign new observations to labeled classes based on the patterns learned from the training data. K-nearest Neighbors algorithm (KNN) is a popular and widely used algorithm for classification, however, its performance can be adversely affected by the presence of outliers in a dataset. In this study we have modified this existing KNN algorithm that can alleviate the effect of outliers in a dataset, thereby improving the performance of the KNN algorithm. We compared the performances of the Modified KNN method and the Existing KNN algorithm …